where \(\mathbf {v}\)  is the score vector, \(\texttt {drop}(\cdot )\)  denotes the dropout strategy [1]} that is used to prevent over-fitting.
As a result, we achieve the parallel scoring with only two layers of fully connected networks (the matrix \(\mathbf {R}\)  can also be regarded as a trainable weight), and reduce the processing steps of actual implementation to \(L \times 1 \times L\) , even better than TPLinker [2]}.
Moreover, the score function conforms the idea of HolE and is capable of capturing the correlation and mutual exclusion between relations, which will be verified in the experiments.
